Review: Applications Status and Key Technologies of Underwater Robots in Fishery
XU Yuliang1, DU Jianghui1, LEI Zeyu1, CAI Yuyan1, YE Zhangying1,2, HAN Zhiying1,2
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; 2. Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Hangzhou 310058, China
许裕良, 杜江辉, 雷泽宇, 蔡雨嫣, 叶章颖, 韩志英. 水下机器人在渔业中的应用现状与关键技术综述[J]. 机器人, 2023, 45(1): 110-128.DOI: 10.13973/j.cnki.robot.210371.
XU Yuliang, DU Jianghui, LEI Zeyu, CAI Yuyan, YE Zhangying, HAN Zhiying. Review: Applications Status and Key Technologies of Underwater Robots in Fishery. ROBOT, 2023, 45(1): 110-128. DOI: 10.13973/j.cnki.robot.210371.
Abstract：The applications status of underwater robots from 2007 to 2021 are summarized from the aspects of fishery environmental monitoring and aquatic animal behaviour monitoring, aquatic animal visual identification and capturing, and aquatic animal living environment maintenance. Key technologies of underwater robots applied in the fishery scenarios are reviewed, the research status and existing problems in structural design and shape optimization of underwater robot, underwater machine vision and image enhancement technology, underwater motion planning, and underwater environment information acquisition and transmission technology are analyzed. Finally, the future research directions of underwater robots dedicated to the fishery scenarios are prospected.
[1] Costello C, Cao L, Gelcich S, et al. The future of food from the sea[J]. Nature, 2020, 588:95-100. [2] 巩沐歌,孟菲良,黄一心,等.中国智能水产养殖发展现状与对策研究[J].渔业现代化, 2018, 45(6):60-66. Gong M G, Meng F L, Huang Y X, et al. Research on development status and countermeasures of intelligent aquaculture in China[J]. Fishery Modernization, 2018, 45(6):60-66. [3] 中华人民共和国农业农村部.关于加快水产养殖机械化发展的意见[EB/OL]. (2020-11-10)[2021-08-14]. http://www.njhs.moa.gov.cn/tzggjzcjd/202011/t202011106356101.htm. Ministry of Agriculture and Rural Affairs of the People's Republic of China. Opinions on accelerating the mechanization development of aquaculture[EB/OL]. (2020-11-10)[2021-08-14]. http://www.njhs.moa.gov.cn/tzggjzcjd/202011/t202011106356101.htm. [4] 甄子健,刘进长.日本最新机器人研发计划及其发展战略[J].机器人技术与应用, 2016(5):14-19. Zhen Z J, Liu J C. Japan's latest robot R&D plan and its development strategy[J]. Robot Technique and Application, 2016(5):14-19. [5] 赵静娟,郑怀国,董瑜,等.全球农业机器人研发趋势预测及对我国的启示[J].中国农机化学报, 2021, 42(4):157-162. Zhao J J, Zheng H G, Dong Y, et al. Forecast research and development trend of international agricultural robot and its suitability to China[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(4):157-162. [6] 夏英凯,朱明,曾鑫,等.水产养殖水下机器人研究进展[J].华中农业大学学报, 2021, 40(3):85-97. Xia Y K, Zhu M, Zeng X, et al. Progress of underwater robot for aquaculture[J]. Journal of Huazhong Agricultural University, 2021, 40(3):85-97. [7] Su X D, Sutarlie L, Loh X J. Sensors, biosensors, and analytical technologies for aquaculture water quality[J]. Research, 2020. DOI:10.34133/2020/8272705. [8] WHOI. Oceanographic system lab (OSL)[EB/OL]. (2020-09-15)[2021-08-18]. https://www2.whoi.edu/site/osl/vehicles/remus-100. [9] Dodge K L, Kukulya A L, Burke E, et al. TurtleCam:A "smart" autonomous underwater vehicle for investigating behaviors and habitats of sea turtles[J]. Frontiers in Marine Science, 2018, 5. DOI:10.3389/fmars.2018.00090. [10] Hawkes L A, Exeter O, Henderson S M, et al. Autonomous underwater videography and tracking of basking sharks[J]. Animal Biotelemetry, 2020, 8. DOI:10.1186/s40317-020-00216-w. [11] Eichhorn M, Ament C, Jacobi M, et al. Modular AUV system with integrated real-time water quality analysis[J]. Sensors, 2018, 18(6). DOI:10.3390/s18061837. [12] Renda F, Giorgio-Serchi F, Boyer F, et al. A unified multi-softbody dynamic model for underwater soft robots[J]. International Journal of Robotics Research, 2018, 37(6):648-666. [13] Yoerger D R, Govindarajan A F, Howland J C, et al. A hybrid underwater robot for multidisciplinary investigation of the ocean twilight zone[J]. Science Robotics, 2021, 6(55). DOI:10.1126/scirobotics.abe1901. [14] Maki T, Horimoto H, Ishihara T, et al. Autonomous tracking of sea turtles based on multibeam imaging sonar:Toward robotic observation of marine life[J]. IFAC-PapersOnLine, 2019, 52(21):86-90. [15] Rohit M H, Barua S, Akter I, et al. IOT based submersible ROV for pisciculture[C]//28th IEEE International Conference on Robot and Human Interactive Communication. Piscataway, USA:IEEE, 2019:1-6. [16] Vasilescu I, Detweiler C, Doniec M, et al. AMOUR V:A hovering energy efficient underwater robot capable of dynamic payloads[J]. International Journal of Robotics Research, 2010, 29(5):547-570. [17] Osen O L, Sandvik R I, Trygstad J B, et al. A novel low cost ROV for aquaculture application[C]//OCEANS. Piscataway, USA:IEEE, 2017. [18] Huang L W, Li Z W, Li S R, et al. Design and application of a free and lightweight aquaculture water quality detection robot[J]. Journal Européen des Systèmes Automatisés, 2020, 53(1): 111-122. [19] Zhang T D, Wang R, Wang Y, et al. Design and locomotion control of a dactylopteridae-inspired biomimetic underwater vehicle with hybrid propulsion[J]. IEEE Transactions on Automation Science and Engineering, 2022, 19(3):2054-2066. [20] Dudek G, Giguere P, Prahacs C, et al. AQUA:An amphibious autonomous robot[J]. Computer, 2007, 40(1):46-53. [21] Katzschmann R K, Delpreto J, Maccurdy R, et al. Exploration of underwater life with an acoustically controlled soft robotic fish[J]. Science Robotics, 2018, 3(16). DOI:10.1126/scirobotics.aar3449. [22] Ravalli A, Rossi C, Marrazza G. Bio-inspired fish robot based on chemical sensors[J]. Sensors and Actuators B:Chemical, 2017, 239:325-329. [23] 程淑红,刘洁,李雷华.基于鱼类运动行为的水质异常评价因子研究[J].仪器仪表学报, 2015, 36(8):1759-1766. Cheng S H, Liu J, Li L H. Study on anomaly water quality assessment factor based on fish movement behavior[J]. Chinese Journal of Scientific Instrument, 2015, 36(8):1759-1766. [24] Takagi M, Mori H, Yimit A, et al. Development of a small size underwater robot for observing fisheries resources-Underwater robot for assisting abalone fishing[J]. Journal of Robotics and Mechatronics, 2016, 28(3):397-403. [25] Ahn J, Yasukawa S, Sonoda T, et al. An optical image transmission system for deep sea creature sampling missions using autonomous underwater vehicle[J]. IEEE Journal of Oceanic Engineering, 2020, 45(2):350-361. [26] Sivčev S, Coleman J, Omerdić E, et al. Underwater manipulators:A review[J]. Ocean Engineering, 2018, 163:431-450. [27] Gong Z Y, Fang X, Chen X Y, et al. A soft manipulator for efficient delicate grasping in shallow water:Modeling, control, and real-world experiments[J]. International Journal of Robotics Research, 2021, 40(1):449-469. [28] 周浩,姜述强,黄海,等.基于视觉感知的海生物吸纳式水下机器人目标捕获控制[J].机器人, 2019, 41(2):242-249,275. Zhou H, Jiang S Q, Huang H, et al. Vision based target capture control for sea organism absorptive underwater vehicle[J]. Robot, 2019, 41(2):242-249,275. [29] Bayse S M, He P G, Pol M V, et al. Quantitative analysis of the behavior of longfin inshore squid (Doryteuthis pealeii) in reaction to a species separation grid of an otter trawl[J]. Fisheries Research, 2014, 152:55-61. [30] Cutkosky M R. On grasp choice, grasp models, and the design of hands for manufacturing tasks[J]. IEEE Transactions on Robotics and Automation, 1989, 5(3):269-279. [31] Takeuchi K, Nomura S, Tamamoto T, et al. Development of multi-joint gripper for underwater operations[C]//OCEANS. Piscataway, USA:IEEE, 2018. DOI:10.1109/OCEANSKOBE. 2018.8558849. [32] Sinatra N R, Teeple C B, Vogt D M, et al. Ultragentle manipulation of delicate structures using a soft robotic gripper[J]. Science Robotics, 2019, 4(33). DOI:10.1126/scirobotics. aax5425. [33] Huang H, Tang Q R, Li J Y, et al. A review on underwater autonomous environmental perception and target grasp, the challenge of robotic organism capture[J]. Ocean Engineering, 2020, 195. DOI:10.1016/j.oceaneng.2019.106644. [34] Yuk H, Lin S T, Ma C, et al. Hydraulic hydrogel actuators and robots optically and sonically camouflaged in water[J]. Nature Communications, 2017, 8. DOI:10.1038/ncomms14230. [35] Stuart H, Wang S Q, Khatib O, et al. The Ocean One hands:An adaptive design for robust marine manipulation[J]. International Journal of Robotics Research, 2017, 36(2):150-166. [36] Galloway K C, Becker K P, Phillips B, et al. Soft robotic grippers for biological sampling on deep reefs[J]. Soft Robotics, 2016, 3(1):23-33. [37] Phillips B T, Becker K P, Kurumaya S, et al. A dexterous, glove-based teleoperable low-power soft robotic arm for delicate deep-sea biological exploration[J]. Scientific Reports, 2018, 8. DOI:10.1038/s41598-018-33138-y. [38] Chaloux N, Phillips B T, Gruber D F, et al. A novel fish sampling system for ROVs[J]. Deep Sea Research, Part I:Oceanographic Research Papers, 2021, 167. DOI:10.1016/j.dsr.2020. 03428. [39] 陈言壮,张奇峰,田启岩,等.水下多指手研究现状[J]. 机器人, 2020, 42(6):749-768. Chen Y Z, Zhang Q F, Tian Q Y, et al. Research status of underwater multi-fingered hands[J]. Robot, 2020, 42(6):749-768. [40] 谢哲新,龚哲元,王田苗,等.可控三维运动的软体驱动器仿真与试验[J].机械工程学报, 2018, 54(21):11-18. Xie Z X, Gong Z Y, Wang T M, et al. Simulation and experiments of a controllable soft spatial fluidic elastomer manipulator[J]. Journal of Mechanical Engineering, 2018, 54(21):11-18. [41] Cai M X, Wang Y, Wang S, et al. Grasping marine products with hybrid-driven underwater vehicle-manipulator system[J]. IEEE Transactions on Automation Science and Engineering, 2020, 17(3):1443-1454. [42] 石建高,余雯雯,赵奎,等.海水网箱网衣防污技术的研究进展[J].水产学报, 2021, 45(3):472-485. Shi J G, Yu W W, Zhao K, et al. Progress in research of antifouling technology of offshore cage netting[J]. Journal of Fisheries of China, 2021, 45(3):472-485. [43] Ohrem S J, Kelasidi E, Bloecher N. Analysis of a novel autonomous underwater robot for biofouling prevention and inspection in fish farms[C]//28th Mediterranean Conference on Control and Automation. Piscataway, USA:IEEE, 2020:1002-1008. [44] Chen Y H, Liu S Y, Fan J C, et al. Novel online optimized control for underwater pipe-cleaning robots[J]. Applied Sciences, 2020, 10(12). DOI:10.3390/app10124279. [45] 庄集超,庞洪臣,刘子浪,等.一种新型深海网箱网衣清洗机器人设计[J].机械, 2018, 45(1):72-75. Zhuang J C, Pang H C, Liu Z L, et al. Design of a new deep-sea net cage cleaning robot[J]. Machinery, 2018, 45(1):72-75. [46] 刘冠灵,卫泓宇,李志鹏,等.履带式深海网箱清洗机器人的设计[J].机械制造, 2019, 57(4):11-14. Liu G L, Wei H Y, Li Z P, et al. Design of crawler-type deepsea cage cleaning robot[J]. Machinery, 2019, 57(4):11-14. [47] 房熊.一种新型水下网箱清洗机器人的设计探索[J].机电设备, 2020, 37(6):57-60,64. Fang X. Design of a new type of underwater cage cleaning robot[J]. Mechanical and Electrical Equipment, 2020, 37(6):57-60,64. [48] 闫银坡,于福杰,陈原.开架式水下机器人水动力系数计算与动力学建模[J].兵工学报, 2021, 42(9):1972-1986. Yan Y B, Yu F J, Chen Y. Hydrodynamic coefficients calculation and dynamic modeling of an open-frame underwater robot[J]. Acta Armamentarii, 2021, 42(9):1972-1986. [49] Zhao Y P, Niu L J, Du H, et al. An adaptive method of damage detection for fishing nets based on image processing technology[J]. Aquacultural Engineering, 2020, 90. DOI:10.1016/j. aquaeng.2020.102071. [50] Koyama T, Yonekura T. Development and experimental results of underwater robot for land aquaculture use[C]//OCEANS. Piscataway, USA:IEEE, 2018. DOI:10.1109/OCEANSKOBE. 2018.8559059. [51] 胡勇兵,黄达,李金刚.鱼池清刷机器人控制系统设计与试验[J].南方农机, 2021, 52(10):7-10. Hu Y B, Huang D, Li J G. Design and experiment of the control system of fish tank cleaning robot[J]. China Southern Agricultural Machinery, 2021, 52(10):7-10. [52] Mahmud M S A, Abidin M S Z, Buyamin S, et al. Multiobjective route planning for underwater cleaning robot in water reservoir tank[J]. Journal of Intelligent & Robotic Systems, 2021, 101. DOI:10.1007/s10846-020-01291-0. [53] Dumiak M. Lice-hunting underwater drone protects salmon[News] [J]. IEEE Spectrum, 2017, 54(4):9-10. [54] Kukulya A, Plueddemann A, Austin T, et al. Under-ice operations with a REMUS-100 AUV in the Arctic[C]//IEEE/OES Autonomous Underwater Vehicles. Piscataway, USA:IEEE, 2010:1-8. [55] 房熊,林礼群,沈熙晟,等.一种渔业水下机器人的系统设计及模型研究[J].渔业现代化, 2018, 45(2):36-41. Fang X, Lin L Q, Shen X S, et al. System design and model research of a fishery underwater robot[J]. Fishery Modernization, 2018, 45(2):36-41. [56] Kirchner F, Straube S, Kühn D, et al. AI technology for underwater robots[M]. Cham, Switzerland:Springer, 2020. [57] 李道亮,包建华.水产养殖水下作业机器人关键技术研究进展[J].农业工程学报, 2018, 34(16):1-9. Li D L, Bao J H. Research progress on key technologies of underwater operation robot for aquaculture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(16):1-9. [58] 张奇峰,张运修,张艾群.深海小型爬行机器人研究现状[J].机器人, 2019, 41(2):250-264. Zhang Q F, Zhang Y X, Zhang A Q. Research status of benthic small-scale crawling robots[J]. Robot, 2019, 41(2):250-264. [59] Wang Y, Wang R, Wang S, et al. Underwater bioinspired propulsion:From inspection to manipulation[J]. IEEE Transactions on Industrial Electronics, 2019, 67(9):7629-7638. [60] 黄琰,李岩,俞建成,等. AUV智能化现状与发展趋势[J].机器人, 2020, 42(2):215-231. Huang Y, Li Y, Yu J C, et al. State-of-the-art and development trend of AUV intelligence[J]. Robot, 2020, 42(2):215-231. [61] Luong J, Glick P, Ong A, et al. Eversion and retraction of a soft robot towards the exploration of coral reefs[C]//2nd IEEE International Conference on Soft Robotics. Piscataway, USA:IEEE, 2019:801-807. [62] Zhang F T, Ennasr O, Litchman E, et al. Autonomous sampling of water columns using gliding robotic fish:Algorithms and harmful-algae-sampling experiments[J]. IEEE Systems Journal, 2016, 10(3):1271-1281. [63] 谢广明,王伟,李亮.智能仿生鱼设计与关键技术[M]. 北京:国防工业出版社, 2020:57-60. Xie G M, Wang W, Li L. Design and key technologies for intelligent biomimetic robotic fish[M]. Beijing:National Defense Industry Press, 2020:57-60. [64] 王延杰,郝牧宇,张霖,等.基于智能驱动材料的水下仿生机器人发展综述[J].水下无人系统学报, 2019, 27(2):123-133. Wang Y J, Hao M Y, Zhang L, et al. Progress of biomimetic underwater robot based on intelligent actuating materials:A review[J]. Journal of Unmanned Undersea Systems, 2019, 27(2):123-133. [65] 梁艺鸣,曹许诺,陈祥平,等."刚-柔"共融型仿生机器鱼[J].中国科学:技术科学, 2018, 48(12):1295-1301. Liang Y M, Cao X N, Chen X P, et al. Bio-inspired rigid-soft coexisting robotic fish[J]. Scientia Sinica Technologica, 2018, 48(12):1295-1301. [66] Li J H, Kim M G, Kang H J, et al. Development of UUV platform and its control method to overcome strong currents:Simulation and experimental studies[J]. IFAC-PapersOnLine, 2018, 51(29):268-273. [67] Gu S X, Guo S X, Zheng L. A highly stable and efficient spherical underwater robot with hybrid propulsion devices[J]. Autonomous Robots, 2020, 44:759-771. [68] Kruusmaa M, Gkliva R, Tuhtan J A, et al. Salmon behavioural response to robots in an aquaculture sea cage[J]. Royal Society Open Science, 2020, 7(3). DOI:10.1098/rsos.191220. [69] Thandiackal R, White C H, Bart-Smith H, et al. Tuna robotics:Hydrodynamics of rapid linear accelerations[J]. Proceedings of the Royal Society B, 2021, 288(1945). DOI:10.1098/rspb. 2020.2726. [70] Li D L, Hao Y F, Duan Y Q. Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish:A review[J]. Reviews in Aquaculture, 2020, 12(3):1390-1411. [71] Wang Z H, Liu C W, Wang S J, et al. UDD:An underwater open-sea farm object detection dataset for underwater robot picking[EB/OL]. (2021-07-28)[2021-08-11]. https://arxiv.org/abs/2003.01446v1. [72] Yuan H C, Zhang S, Qin E Q, et al. Aquatic animal image classification technology based on transfer learning and data augmentation[J]. Journal of Coastal Research, 2020, 105(S1):129-133. [73] Chen L, Tong L, Zhou F X, et al. A benchmark dataset for both underwater image enhancement and underwater object detection[EB/OL]. (2020-06-29)[2021-08-14]. https://arxiv.org/abs/2006.15789. [74] Huang H, Zhou H, Yang X, et al. Faster R-CNN for marine organisms detection and recognition using data augmentation[J]. Neurocomputing, 2019, 337:372-384. [75] 赵德安,刘晓洋,孙月平,等.基于机器视觉的水下河蟹识别方法[J].农业机械学报, 2019, 50(3):151-158. Zhao D A, Liu X Y, Sun Y P, et al. Detection of underwater crabs based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(3):151-158. [76] 赵德安,曹硕,孙月平,等.基于联动扩展神经网络的水下自由活蟹检测器研究[J].农业机械学报, 2020, 51(9):163-174. Zhao D A, Cao S, Sun Y P, et al. Small-sized efficient detector for underwater freely live crabs based on compound scaling neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(9):163-174. [77] Liu X F, Jia Z Q, Hou X K, et al. Real-time marine animal images classification by embedded system based on mobilenet and transfer learning[C]//OCEANS. Piscataway, USA:IEEE, 2019. DOI:10.1109/OCEANSE.2019.8867190. [78] 孙龙清,刘婷,陈帅华,等.多目标鱼体对象提议检测算法研究[J].农业机械学报, 2019, 50(12):260-267. Sun L Q, Liu T, Chen S H, et al. Multi-target fish detection algorithm based on object proposals[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12):260-267. [79] Qiao X, Bao J H, Zhang H, et al. Underwater sea cucumber identification based on principal component analysis and support vector machine[J]. Measurement, 2019, 133:444-455. [80] 俞伟聪,郭显久,刘钰发,等.基于轻量化深度学习Mobilenet-SSD网络模型的海珍品检测方法[J].大连海洋大学学报, 2021, 36(2):340-346. Yu W C, Guo X J, Liu Y F, et al. Detection method of high value marine food organisms based on lightweight deep learning Mobilenet-SSD network[J]. Journal of Dalian Ocean University, 2021, 36(2):340-346. [81] Cao S, Zhao D A, Liu X Y, et al. Real-time robust detector for underwater live crabs based on deep learning[J]. Computers and Electronics in Agriculture, 2020, 172. DOI:10.1016/j. compag.2020.105339. [82] Han M, Lyu Z, Qiu T, et al. A review on intelligence dehazing and color restoration for underwater images[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020, 50(5):1820-1832. [83] 于红.水产动物目标探测与追踪技术及应用研究进展[J]. 大连海洋大学学报, 2020, 35(6):793-804. Yu H. Research progress on object detection and tracking techniques utilization in aquaculture:A review[J]. Journal of Dalian Ocean University, 2020, 35(6):793-804. [84] 袁红春,张硕.基于Faster R-CNN和图像增强的水下鱼类目标检测方法[J].大连海洋大学学报, 2020, 35(4):612-619. Yuan H C, Zhang S. Detection of underwater fish based on Faster R-CNN and image enhancement[J]. Journal of Dalian Ocean University, 2020, 35(4):612-619. [85] McGlamery B L. A computer model for underwater camera systems[C]//Ocean Optics VI. Bellingham, USA:SPIE, 1980. DOI:10.1117/12.958279. [86] Narasimhan S G, Nayar S K. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48:233-254. [87] 林森,白莹,李文涛,等.基于修正模型与暗通道先验信息的水下图像复原[J].机器人, 2020, 42(4):427-435, 447. Lin S, Bai Y, Li W T, et al. Underwater image restoration based on the modified model and dark channel prior[J]. Robot, 2020, 42(4):427-435,447. [88] 王丹,张子玉,赵金宝,等.基于场景深度估计的自然光照水下图像增强方法[J].机器人, 2021, 43(3):364-372. Wang D, Zhang Z Y, Zhao J B, et al. An enhancement method for underwater images under natural illumination based on scene depth estimation[J]. Robot, 2021, 43(3):364-372. [89] Akkaynak D, Treibitz T. Sea-thru:A method for removing water from underwater images[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2019:1682-1691. [90] Anwar S, Li C Y. Diving deeper into underwater image enhancement:A survey[J]. Signal Processing:Image Communication, 2020, 89. DOI:10.1016/j.image.2020.115978. [91] Chen X Y, Yu J Z, Kong S H, et al. Towards real-time advancement of underwater visual quality with GAN[J]. IEEE Transactions on Industrial Electronics, 2019, 66(12):9350-9359. [92] Guo P F, Zeng D L, Tian Y B, et al. Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling[J]. Computers and Electronics in Agriculture, 2020, 175. DOI:10.1016/j.compag.2020.105608. [93] Islam M J, Xia Y Y, Sattar J. Fast underwater image enhancement for improved visual perception[J]. IEEE Robotics and Automation Letters, 2020, 5(2):3227-3234. [94] Krishnaraj N, Elhoseny M, Thenmozhi M, et al. Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)[J]. Journal of Real-Time Image Processing, 2020, 17:2097-2111. [95] Aguirre-Castro O A, Inzunza-González E, García-Guerrero E E, et al. Design and construction of an ROV for underwater exploration[J]. Sensors, 2019, 19(24). DOI:10.3390/s19245387. [96] 段延娥,李道亮,李振波,等.基于计算机视觉的水产动物视觉特征测量研究综述[J].农业工程学报, 2015, 31(15):1-11. Duan Y E, Li D L, Li Z B, et al. Review on visual attributes measurement research of aquatic animals based on computer vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(15):1-11. [97] 徐建瑜,崔绍荣,苗香雯,等.计算机视觉技术在水产养殖中的应用与展望[J].农业工程学报, 2005, 21(8):174-178. Xu J Y, Cui S R, Miao X W, et al. Application and prospect of computer vision technology in aquaculture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(8):174-178. [98] 郑金存,赵峰,林勇,等.基于近红外深度图的游泳型鱼类摄食强度实时测量[J].上海海洋大学学报, 2021, 30(6):1067-1078. Zheng J C, Zhao F, Lin Y, et al. Evaluation of fish feeding intensity in aquaculture based on near-infrared depth image[J]. Journal of Shanghai Ocean University, 2021, 30(6):1067- 1078. [99] 徐玉如,李彭超.水下机器人发展趋势[J].自然杂志, 2011, 33(3):125-132. Xu Y R, Li P C. Developing tendency of unmanned underwater vehicles[J]. Chinese Journal of Nature, 2011, 33(3):125-132. [100] Cai M X, Wang S, Wang Y, et al. Coordinated control of underwater biomimetic vehicle-manipulator system for free floating autonomous manipulation[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2021, 51(8):4793-4803. [101] Niu H L, Ji Z, Savvaris A, et al. Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment[J]. Ocean Engineering, 2020, 196. DOI:10.1016/j.oceaneng.2019.106766. [102] 王梓强,胡晓光,李晓筱,等.移动机器人全局路径规划算法综述[J].计算机科学, 2021, 48(10):19-29. Wang Z Q, Hu X G, Li X X, et al. Overview of global path planning algorithms for mobile robots[J]. Computer Science, 2021, 48(10):19-29. [103] Sandoy S S, Hegde J, Schjolberg I, et al. Polar map:A digital representation of closed structures for underwater robotic inspection[J]. Aquacultural Engineering, 2020, 89. DOI:10.1016/j.aquaeng.2019.102039. [104] Petillot Y R, Antonelli G, Casalino G, et al. Underwater robots:From remotely operated vehicles to intervention-autonomous underwater vehicles[J]. IEEE Robotics & Automation Magazine, 2019, 26(2):94-101. [105] 赵小川,罗庆生,韩宝玲.机器人多传感器信息融合研究综述[J].传感器与微系统, 2008, 27(8):1-4,11. Zhao X C, Luo Q S, Han B L. Review of robot multi-sensor information fusion[J]. Transducer and Microsystem Technologies, 2008, 27(8):1-4,11. [106] 聂鹏程,张慧,耿洪良,等.农业物联网技术现状与发展趋势[J].浙江大学学报(农业与生命科学版), 2021, 47(2):135-146. Nie P C, Zhang H, Geng H L, et al. Current situation and development trend of agricultural Internet of Things technology[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(2):135-146. [107] Hu Z H, Zhang Y R, Zhao Y C, et al. A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture[J]. Sensors, 2019, 19(6). DOI:10.3390/s19061420. [108] Deng Y. Uncertainty measure in evidence theory[J]. Science China:Information Sciences, 2020, 63(11). DOI:10.1007/s11432-020-3006-9. [109] Shaukat N, Ali A, Iqbal M J, et al. Multi-sensor fusion for underwater vehicle localization by augmentation of RBF neural network and error-state Kalman filter[J]. Sensors, 2021, 21(4). DOI:10.3390/s21041149. [110] Xiao F Y. A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion[J]. Information Sciences, 2020, 514:462-483. [111] 赵炳巍,曹岩,贾峰,等.移动机器人多传感器信息融合方法综述[J].电子测试, 2020(18):68-69. Zhao B W, Cao Y, Jia F, et al. Survey of multi-sensor information fusion methods for mobile robots[J]. Electronic Test, 2020(18):68-69. [112] Terayama K, Shin K, Mizuno K, et al. Integration of sonar and optical camera images using deep neural network for fish monitoring[J]. Aquacultural Engineering, 2019, 86. DOI:10.1016/j.aquaeng.2019.102000. [113] Remmas W, Chemori A, Kruusmaa M. Diver tracking in open waters:A low-cost approach based on visual and acoustic sensor fusion[J]. Journal of Field Robotics, 2021, 38(3):494-508.
